Methodology article

A tree-based conservation scoring method for short linear motifs in multiple alignments of protein sequences

Claudia Chica¹ , Alberto Labarga² , Cathryn M Gould¹ , Rodrigo López² and Toby J Gibson¹

¹EMBL Structural and Computational Biology Unit, Meyerhofstrasse 1, 69117 Heidelberg, Germany

²EBI European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK

author email corresponding author email

BMC Bioinformatics 2008, 9:229doi:10.1186/1471-2105-9-229

Published:	6 May 2008

Abstract

Background

The structure of many eukaryotic cell regulatory proteins is highly modular. They are assembled from globular domains, segments of natively disordered polypeptides and short linear motifs. The latter are involved in protein interactions and formation of regulatory complexes. The function of such proteins, which may be difficult to define, is the aggregate of the subfunctions of the modules. It is therefore desirable to efficiently predict linear motifs with some degree of accuracy, yet sequence database searches return results that are not significant.

Results

We have developed a method for scoring the conservation of linear motif instances. It requires only primary sequence-derived information (e.g. multiple alignment and sequence tree) and takes into account the degenerate nature of linear motif patterns. On our benchmarking, the method accurately scores 86% of the known positive instances, while distinguishing them from random matches in 78% of the cases. The conservation score is implemented as a real time application designed to be integrated into other tools. It is currently accessible via a Web Service or through a graphical interface.

Conclusion

The conservation score improves the prediction of linear motifs, by discarding those matches that are unlikely to be functional because they have not been conserved during the evolution of the protein sequences. It is especially useful for instances in non-structured regions of the proteins, where a domain masking filtering strategy is not applicable.